{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a Knowledge Graph using an LLM"
      ],
      "metadata": {
        "id": "PxmHKY0LCH_u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing the dependencies"
      ],
      "metadata": {
        "id": "kXFdBGF8CLiY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "jMSUePioNyAj",
        "outputId": "80e923ea-4c9d-48dc-8cce-ba7776090afb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting mirascope[openai]\n",
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            "Downloading mirascope-1.25.4-py3-none-any.whl (373 kB)\n",
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            "\u001b[?25hInstalling collected packages: mirascope\n",
            "Successfully installed mirascope-1.25.4\n"
          ]
        }
      ],
      "source": [
        "!pip install \"mirascope[openai]\" matplotlib networkx beautifulsoup4"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## OpenAI API Key\n",
        "To get an OpenAI API key, visit https://platform.openai.com/settings/organization/api-keys and generate a new key. If you're a new user, you may need to add billing details and make a minimum payment of $5 to activate API access."
      ],
      "metadata": {
        "id": "ITxsF9UACcqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "os.environ[\"OPENAI_API_KEY\"] = getpass('Enter OpenAI API Key: ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ati5x2KOSfc",
        "outputId": "adaced61-6df3-4efc-87eb-dc99115fd9ef"
      },
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter OpenAI API Key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining Graph Schema"
      ],
      "metadata": {
        "id": "ip9KPJzFCxy5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pydantic import BaseModel, Field\n",
        "\n",
        "class Edge(BaseModel):\n",
        "    source: str\n",
        "    target: str\n",
        "    relationship: str\n",
        "\n",
        "class Node(BaseModel):\n",
        "    id: str\n",
        "    type: str\n",
        "    properties: dict | None = None\n",
        "\n",
        "class KnowledgeGraph(BaseModel):\n",
        "    nodes: list[Node]\n",
        "    edges: list[Edge]\n"
      ],
      "metadata": {
        "id": "oM9GjlxhOow5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the Patient Log"
      ],
      "metadata": {
        "id": "0JsYt1HtC0kv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "patient_log = \"\"\"\n",
        "Mary called for help at 3:45 AM, reporting that she had fallen while going to the bathroom. This marks the second fall incident within a week. She complained of dizziness before the fall.\n",
        "\n",
        "Earlier in the day, Mary was observed wandering the hallway and appeared confused when asked basic questions. She was unable to recall the names of her medications and asked the same question multiple times.\n",
        "\n",
        "Mary skipped both lunch and dinner, stating she didn’t feel hungry. When the nurse checked her room in the evening, Mary was lying in bed with mild bruising on her left arm and complained of hip pain.\n",
        "\n",
        "Vital signs taken at 9:00 PM showed slightly elevated blood pressure and a low-grade fever (99.8°F). Nurse also noted increased forgetfulness and possible signs of dehydration.\n",
        "\n",
        "This behavior is similar to previous episodes reported last month.\n",
        "\"\"\"\n"
      ],
      "metadata": {
        "id": "p5O2QJ9HOp9q"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generating the Knowledge Graph"
      ],
      "metadata": {
        "id": "0AInwuqOC5TP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mirascope.core import openai, prompt_template\n",
        "\n",
        "@openai.call(model=\"gpt-4o-mini\", response_model=KnowledgeGraph)\n",
        "@prompt_template(\n",
        "    \"\"\"\n",
        "    SYSTEM:\n",
        "    Extract a knowledge graph from this patient log.\n",
        "    Use Nodes to represent people, symptoms, events, and observations.\n",
        "    Use Edges to represent relationships like \"has symptom\", \"reported\", \"noted\", etc.\n",
        "\n",
        "    The log:\n",
        "    {log_text}\n",
        "\n",
        "    Example:\n",
        "    Mary said help, I've fallen.\n",
        "    Node(id=\"Mary\", type=\"Patient\", properties={{}})\n",
        "    Node(id=\"Fall Incident 1\", type=\"Event\", properties={{\"time\": \"3:45 AM\"}})\n",
        "    Edge(source=\"Mary\", target=\"Fall Incident 1\", relationship=\"reported\")\n",
        "    \"\"\"\n",
        ")\n",
        "def generate_kg(log_text: str) -> openai.OpenAIDynamicConfig:\n",
        "    return {\"log_text\": log_text}\n"
      ],
      "metadata": {
        "id": "0mMRb2toPN2m"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kg = generate_kg(patient_log)\n",
        "print(kg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Vjx2DK-PXGk",
        "outputId": "f42cdb84-dbf4-4e97-ed4d-c076f77bb812"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nodes=[Node(id='Mary', type='Patient', properties={}), Node(id='Fall Incident 1', type='Event', properties={'time': '3:45 AM'}), Node(id='Disorientation', type='Symptom', properties={}), Node(id='Skipped Lunch', type='Event', properties={}), Node(id='Bruising', type='Observation', properties={'description': 'slight bruising on her arm'})] edges=[Edge(source='Mary', target='Fall Incident 1', relationship='reported'), Edge(source='Mary', target='Disorientation', relationship='has symptom'), Edge(source='Mary', target='Skipped Lunch', relationship='reported'), Edge(source='Nurse', target='Bruising', relationship='noted'), Edge(source='Mary', target='Bruising', relationship='has observation')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Querying the graph"
      ],
      "metadata": {
        "id": "lXU2UMOwC_lV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@openai.call(model=\"gpt-4o-mini\")\n",
        "@prompt_template(\n",
        "    \"\"\"\n",
        "    SYSTEM:\n",
        "    Use the knowledge graph to answer the user’s question.\n",
        "\n",
        "    Graph:\n",
        "    {knowledge_graph}\n",
        "\n",
        "    USER:\n",
        "    {question}\n",
        "    \"\"\"\n",
        ")\n",
        "def run(question: str, knowledge_graph: KnowledgeGraph): ...\n"
      ],
      "metadata": {
        "id": "FUpytSVuRHlN"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What health risks or concerns does Mary exhibit based on her recent behavior and vitals?\"\n",
        "print(run(question, kg))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPgbxQfpRJXl",
        "outputId": "cea5aacc-7f4a-4c0a-e670-d2053ae8570f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Based on the information provided, Mary exhibits several health risks and concerns:\n",
            "\n",
            "1. **Fall Incident**: Mary reported a fall incident at 3:45 AM, which could indicate issues with balance, coordination, or possible underlying health conditions that need to be addressed.\n",
            "\n",
            "2. **Disorientation**: She has reported experiencing disorientation. This symptom could be related to various health issues, including neurological concerns or medication side effects.\n",
            "\n",
            "3. **Skipped Meals**: The report of skipping lunch could suggest a lack of appetite, possible cognitive issues, or neglecting self-care, which can lead to nutritional deficits.\n",
            "\n",
            "4. **Bruising**: The observation of slight bruising on her arm indicates potential trauma from the fall incident or may signal other underlying issues such as blood clotting problems.\n",
            "\n",
            "These factors combined may point to increased vulnerability and a need for further assessment and monitoring of her health and safety.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing the Graph"
      ],
      "metadata": {
        "id": "WGL-6-yoDBt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "\n",
        "def render_graph(kg: KnowledgeGraph):\n",
        "    G = nx.DiGraph()\n",
        "\n",
        "    for node in kg.nodes:\n",
        "        G.add_node(node.id, label=node.type, **(node.properties or {}))\n",
        "\n",
        "    for edge in kg.edges:\n",
        "        G.add_edge(edge.source, edge.target, label=edge.relationship)\n",
        "\n",
        "    plt.figure(figsize=(15, 10))\n",
        "    pos = nx.spring_layout(G)\n",
        "    nx.draw_networkx_nodes(G, pos, node_size=2000, node_color=\"lightgreen\")\n",
        "    nx.draw_networkx_edges(G, pos, arrowstyle=\"->\", arrowsize=20)\n",
        "    nx.draw_networkx_labels(G, pos, font_size=12, font_weight=\"bold\")\n",
        "    edge_labels = nx.get_edge_attributes(G, \"label\")\n",
        "    nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color=\"blue\")\n",
        "    plt.title(\"Healthcare Knowledge Graph\", fontsize=15)\n",
        "    plt.show()\n",
        "\n",
        "render_graph(kg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "NByAlY1fRK2-",
        "outputId": "d3746a64-13a3-4802-ce54-713f0066d558"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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    }
  ]
}